Machine learning-based prediction of axial load and strain capacities for circular FRP-concrete-steel double-skin tubular columns

Abstract

Fiber-reinforced polymer (FRP)-concrete-steel double-skin tubular columns (hybrid DSTCs) are innovative composite columns offering high strength, ductility, corrosion resistance, and lightweight due to hollow cross-section. Despite extensive experimental, numerical, and analytical studies, accurately predicting the behavior using traditional methods remains challenging. Experimental and numerical methods are costly and time-consuming, while analytical approaches are conservative and may not effectively capture complex and nonlinear relationships. This study compares five machine learning (ML) models with two existing empirical equations for predicting the axial load and axial strain of circular hybrid DSTCs. An extensive dataset of 249 specimens from the literature was used to train and test ML models. Five ML models, namely, multiple linear regression (MLR), decision tree (DT), random forest (RF), K-nearest neighbors (KNNs), and extreme gradient boosting (XGBoost), were trained using eight input parameters. Results indicate that the XGBoost model achieved the highest accuracy in predicting both axial load and strain capacities, with R2 values of 0.87 and 0.96, respectively. Among the empirical equations, Louk Fanggi and Ozbakkaloglu’s equation performed better than traditional ML models such as MLR and DT for axial load prediction, achieving an R2 value of 0.785, compared to 0.72 for MLR and 0.74 for DT. Feature importance analysis further identified the significant influence of geometric parameters on axial load prediction and material properties on axial strain prediction. Additionally, a user-friendly web application is developed, allowing users to easily predict the axial load and strain of circular hybrid DSTCs, demonstrating ML’s efficiency as a data-driven alternative to empirical approaches.